Science & Engineering Librarian
Denise Wetzel
she/her/hers
Contact:
206 Davey Lab
Physical & Mathematical Sciences Library
University Park
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Physical & Mathematical Sciences Library
University Park
Research Starters for the Statistical Modeling Data Sciences Option
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MathSciNet This link opens in a new windowMathSciNet is a comprehensive database covering the world's mathematical literature of the past 61 years. It provides Web access to reviews and bibliographic data from Mathematical Review and Current Mathematical Publications. It provides links to original articles and free access to Featured Reviews.
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The American StatisticianThe American Statistician publishes articles on statistics, statistical practice, statistics teaching, and statistical computing and graphics.
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Journal of Statistics and Data Science EducationThe Journal of Statistics and Data Science Education (JSDSE) is an open access peer-reviewed journal published by the American Statistical Association.
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Statistical Analysis and Data MiningStatistical Analysis and Data Mining addresses the broad area of data analysis, including statistical approaches, machine learning, data mining, and applications. Topics include statistical and computational approaches for analyzing massive and complex datasets, novel statistical and/or machine learning methods and theory, and state-of-the-art applications with high impact.
Books on Computational Statistics
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Methodological and Applied Statistics and Demography I [electronic resource] : SIS 2024, Short Papers, Plenary and Specialized Sessions
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This book of peer-reviewed short papers on methodological and applied statistics and demography is the first of four volumes from the 52nd Scientific Meeting of the Italian Statistical Society (SIS 2024), held in Bari, Italy, on June 17-20, 2024. It features invited contributions presented in the Plenary and Specialized Sessions. The volumes address a large number of topics and applications of current interest. The topics covered include, but are not limited to, statistical theory and methods, sampling theory, Bayesian statistics, statistical modeling, computational statistics, classification, data analysis, gender statistics and applied statistics. The applications reflect new analyses in a wide variety of fields, including demography, psychometrics, education, business, economics, finance, law, and other social sciences and humanities, epidemiology, the life and health sciences as well as the environmental and natural sciences and engineering. This variety also demonstrates the important role of statistical science in addressing the societal and environmental challenges of sustainable development. One of the aims of the Italian Statistical Society (SIS) is to promote scientific activities for the development of statistical sciences. Its biennial international Scientific Meeting represents the Society's largest event which brings together national and international researchers and professionals to exchange ideas and discuss recent advances and developments in theoretical and applied statistics.
ISBN: 9783031643460Publication Date: 2025 -
Methodological and Applied Statistics and Demography II [electronic resource] : SIS 2024, Short Papers, Solicited Sessions
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This book of peer-reviewed short papers on methodological and applied statistics and demography is the second of four volumes from the 52nd Scientific Meeting of the Italian Statistical Society (SIS 2024), held in Bari, Italy, on June 17-20, 2024. It features invited contributions presented in the Solicited Sessions. The volumes address a large number of topics and applications of current interest. The topics covered include, but are not limited to, statistical theory and methods, sampling theory, Bayesian statistics, statistical modeling, computational statistics, classification, data analysis, gender statistics and applied statistics. The applications reflect new analyses in a wide variety of fields, including demography, psychometrics, education, business, economics, finance, law, and other social sciences and humanities, epidemiology, the life and health sciences as well as the environmental and natural sciences and engineering. This variety also demonstrates the important role of statistical science in addressing the societal and environmental challenges of sustainable development. One of the aims of the Italian Statistical Society (SIS) is to promote scientific activities for the development of statistical sciences. Its biennial international Scientific Meeting represents the Society's largest event which brings together national and international researchers and professionals to exchange ideas and discuss recent advances and developments in theoretical and applied statistics.
ISBN: 9783031643507Publication Date: 2025 -
Statistical Modeling and Computation
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This book, Statistical Modeling and Computation, provides a unique introduction to modern statistics from both classical and Bayesian perspectives. It also offers an integrated treatment of mathematical statistics and modern statistical computation, emphasizing statistical modeling, computational techniques, and applications. The 2nd edition changes the programming language used in the text from MATLAB to Julia. For all examples with computing components, the authors provide data sets and their own Julia codes. The new edition features numerous full color graphics to illustrate the concepts discussed in the text, and adds three entirely new chapters on a variety of popular topics, including: Regularization and the Lasso regression Bayesian shrinkage methods Nonparametric statistical tests Splines and the Gaussian process regression Joshua C. C. Chan is Professor of Economics, and holds the endowed Olson Chair at Purdue University. He is an elected fellow at the International Association for Applied Econometrics and served as Chair for the Economics, Finance and Business Section of the International Society for Bayesian Analysis from 2020-2022. His research focuses on building new high-dimensional time-series models and developing efficient estimation methods for these models. He has published over 50 papers in peer-reviewed journals, including some top-field journals such as Journal of Econometrics, Journal of the American Statistical Association and Journal of Business and Economic Statistics. Dirk Kroese is Professor of Mathematics and Statistics at the University of Queensland. He is known for his significant contributions to the fields of applied probability, mathematical statistics, machine learning, and Monte Carlo methods. He has published over 140 articles and 7 books. He is a pioneer of the well-known Cross-Entropy (CE) method, which is being used around the world to help solve difficult estimation and optimization problems in science, engineering, and finance. In addition to his scholarly contributions, Dirk Kroese is recognized for his role as an educator and mentor, having supervised and inspired numerous students and researchers.
ISBN: 9781071641323Publication Date: 2025 -
Statistical Inference and Machine Learning for Big Data
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This book presents a variety of advanced statistical methods at a level suitable for advanced undergraduate and graduate students as well as for others interested in familiarizing themselves with these important subjects. It proceeds to illustrate these methods in the context of real-life applications in a variety of areas such as genetics, medicine, and environmental problems. The book begins in Part I by outlining various data types and by indicating how these are normally represented graphically and subsequently analyzed. In Part II, the basic tools in probability and statistics are introduced with special reference to symbolic data analysis. The most useful and relevant results pertinent to this book are retained. In Part III, the focus is on the tools of machine learning whereas in Part IV the computational aspects of BIG DATA are presented. This book would serve as a handy desk reference for statistical methods at the undergraduate and graduate level as well as be useful in courses which aim to provide an overview of modern statistics and its applications.
ISBN: 9783031067846Publication Date: 2022
Books on Vector Analysis
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Advances in Machine Learning and Big Data Analytics I
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This edited volume on machine learning and big data analytics (Proceedings of ICMLBDA 2023, that was held on May 29-30, 2023 by NERIST and NIT Arunachal Pradesh India) is intended to be used as a reference book for researchers and professionals to share their research and reports of new technologies and applications in Machine Learning and Big Data Analytics like biometric Recognition Systems, medical diagnosis, industries, telecommunications, AI Petri Nets Model-Based Diagnosis, gaming, stock trading, Intelligent Aerospace Systems, robot control, law, remote sensing and scientific discovery agents and multiagent systems; and natural language and Web intelligence. The intent of this book is to provide awareness of algorithms used for machine learning and big data in the advanced Scientific Technologies, provide a correlation of multidisciplinary areas and become a point of great interest for Data Scientists, systems architects, developers, new researchers and graduate level students. This volume provides cutting-edge research from around the globe on this field. Current status, trends, future directions, opportunities, etc. are discussed, making it friendly for beginners and young researchers.
ISBN: 9783031513381Publication Date: 2025 -
Extreme Value Theory for Time Series [electronic resource] : Models with Power-Law Tails
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This book deals with extreme value theory for univariate and multivariate time series models characterized by power-law tails. These include the classical ARMA models with heavy-tailed noise and financial econometrics models such as the GARCH and stochastic volatility models. Rigorous descriptions of power-law tails are provided through the concept of regular variation. Several chapters are devoted to the exploration of regularly varying structures. The remaining chapters focus on the impact of heavy tails on time series, including the study of extremal cluster phenomena through point process techniques. A major part of the book investigates how extremal dependence alters the limit structure of sample means, maxima, order statistics, sample autocorrelations. This text illuminates the theory through hundreds of examples and as many graphs showcasing its applications to real-life financial and simulated data. The book can serve as a text for PhD and Master courses on applied probability, extreme value theory, and time series analysis. It is a unique reference source for the heavy-tail modeler. Its reference quality is enhanced by an exhaustive bibliography, annotated by notes and comments making the book broadly and easily accessible.
ISBN: 9783031591563Publication Date: 2024 -
Matrix Algebra [electronic resource] : Theory, Computations and Applications in Statistics
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This book presents the theory of matrix algebra for statistical applications, explores various types of matrices encountered in statistics, and covers numerical linear algebra. Matrix algebra is one of the most important areas of mathematics in data science and in statistical theory, and previous editions had essential updates and comprehensive coverage on critical topics in mathematics. This 3rd edition offers a self-contained description of relevant aspects of matrix algebra for applications in statistics. It begins with fundamental concepts of vectors and vector spaces; covers basic algebraic properties of matrices and analytic properties of vectors and matrices in multivariate calculus; and concludes with a discussion on operations on matrices, in solutions of linear systems and in eigenanalysis. It also includes discussions of the R software package, with numerous examples and exercises. Matrix Algebra considers various types of matrices encountered in statistics, such as projection matrices and positive definite matrices, and describes special properties of those matrices; as well as describing various applications of matrix theory in statistics, including linear models, multivariate analysis, and stochastic processes. It begins with a discussion of the basics of numerical computations and goes on to describe accurate and efficient algorithms for factoring matrices, how to solve linear systems of equations, and the extraction of eigenvalues and eigenvectors. It covers numerical linear algebra-one of the most important subjects in the field of statistical computing. The content includes greater emphases on R, and extensive coverage of statistical linear models. Matrix Algebra is ideal for graduate and advanced undergraduate students, or as a supplementary text for courses in linear models or multivariate statistics. It's also ideal for use in a course in statistical computing, or as a supplementary text for various courses that emphasize computations.
ISBN: 9783031421440Publication Date: 2024 -
15 Math Concepts Every Data Scientist Should Know
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Create more effective and powerful data science solutions by learning when, where, and how to apply key math principles that drive most data science algorithms Key Features Understand key data science algorithms with Python-based examples Increase the impact of your data science solutions by learning how to apply existing algorithms Take your data science solutions to the next level by learning how to create new algorithms Purchase of the print or Kindle book includes a free PDF eBook Book Description Data science combines the power of data with the rigor of scientific methodology, with mathematics providing the tools and frameworks for analysis, algorithm development, and deriving insights. As machine learning algorithms become increasingly complex, a solid grounding in math is crucial for data scientists. David Hoyle, with over 30 years of experience in statistical and mathematical modeling, brings unparalleled industrial expertise to this book, drawing from his work in building predictive models for the world's largest retailers. Encompassing 15 crucial concepts, this book covers a spectrum of mathematical techniques to help you understand a vast range of data science algorithms and applications. Starting with essential foundational concepts, such as random variables and probability distributions, you'll learn why data varies, and explore matrices and linear algebra to transform that data. Building upon this foundation, the book spans general intermediate concepts, such as model complexity and network analysis, as well as advanced concepts such as kernel-based learning and information theory. Each concept is illustrated with Python code snippets demonstrating their practical application to solve problems. By the end of the book, you'll have the confidence to apply key mathematical concepts to your data science challenges. What you will learn Master foundational concepts that underpin all data science applications Use advanced techniques to elevate your data science proficiency Apply data science concepts to solve real-world data science challenges Implement the NumPy, SciPy, and scikit-learn concepts in Python Build predictive machine learning models with mathematical concepts Gain expertise in Bayesian non-parametric methods for advanced probabilistic modeling Acquire mathematical skills tailored for time-series and network data types Who this book is for This book is for data scientists, machine learning engineers, and data analysts who already use data science tools and libraries but want to learn more about the underlying math. Whether you're looking to build upon the math you already know, or need insights into when and how to adopt tools and libraries to your data science problem, this book is for you. Organized into essential, general, and selected concepts, this book is for both practitioners just starting out on their data science journey and experienced data scientists.
ISBN: 9781837634187Publication Date: 2024-08-30
